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Computational science requires translation, breaking ideas and principles into pieces that algorithms can parse. The work requires experts capable of zooming in on core computer science while also being able to step back and make sure that the big scientific questions are addressed.
This guest, Sunita Chandrasekaran of the University of Delaware, moves seamlessly across these layers— from working with students and postdocs on fundamental software, collaborating with researchers on questions ranging from physics to art conservation and helping to shape AI policy in her state. In our conversation, we discuss the rapid pace of artificial intelligence, the synergy among academia, the national labs and industry, and keeping humans at the center of AI innovation.
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By Krell Institute5
33 ratings
Computational science requires translation, breaking ideas and principles into pieces that algorithms can parse. The work requires experts capable of zooming in on core computer science while also being able to step back and make sure that the big scientific questions are addressed.
This guest, Sunita Chandrasekaran of the University of Delaware, moves seamlessly across these layers— from working with students and postdocs on fundamental software, collaborating with researchers on questions ranging from physics to art conservation and helping to shape AI policy in her state. In our conversation, we discuss the rapid pace of artificial intelligence, the synergy among academia, the national labs and industry, and keeping humans at the center of AI innovation.
You'll meet: